Practical privacy & safety patterns for adding AI to your website or app (for non‑engineers)
Adding AI to a website or mobile app can improve support, speed up workflows, and create genuinely better user experiences. But there’s a trade‑off: the moment users feel unsure about how their data is handled-or whether the AI is “making things up”-trust drops quickly.
At Jensen Technologies, we’ve delivered and maintained web and app products for many years. What we’ve learned is simple: teams that ship AI successfully treat privacy and safety as product requirements, not legal afterthoughts.
Below is a practical set of safeguards you can request in a proposal or contract. They’re written for non‑engineers, but concrete enough to test and sign off.
1) Clear disclosure: what the AI is doing (and what it isn’t)
Users should not have to hunt through a privacy policy to understand an AI feature. Ask for short, in‑context language near the feature explaining:
- Purpose: what the AI is for (e.g., “summarises your notes”)
- Limits: what it can’t guarantee (e.g., “may be inaccurate”)
- Data used: what information is sent to the AI provider
Acceptance criteria: a first‑time user can read the disclosure and understand it in under 10 seconds.
2) Consent for sensitive data (and a real opt‑out)
If users might paste or upload sensitive information-personal IDs, health details, HR data, client information-require an explicit consent step (checkbox or inline confirmation) and an opt‑out path.
Acceptance criteria: opting out does not break the app; the AI feature either runs in a reduced mode or is replaced with a non‑AI alternative.
3) Prompt & output retention rules you can measure
“We don’t store data” is rarely specific enough. Decide up front:
- Are prompts stored? For how long?
- Are AI outputs stored? For how long?
- Who can access logs and support tooling?
Acceptance criteria: retention settings are documented and verifiable (e.g., visible in the vendor configuration or system logs), and your team can export/delete relevant data on request.
4) Prompt redaction: mask sensitive data before it leaves your system
A simple but powerful pattern is redaction: automatically removing or masking common sensitive fields before sending text to an AI provider (emails, phone numbers, addresses, account IDs, etc.).
Acceptance criteria: test prompts containing sample PII are masked in the outbound request and in any stored logs.
5) Model/vendor provenance: know what you’re using
Ask your delivery team to document:
- Which model/provider is used
- Where processing occurs (region)
- Any training/data‑use guarantees that apply
- How and when provider/model changes will be communicated
Acceptance criteria: this information appears in the handover pack and is updated when the system changes.
6) Rate limits, timeouts, and fallbacks (so AI failure doesn’t equal product failure)
AI services can be slow, unavailable, or unexpectedly expensive under load. Require:
- Rate limiting to prevent abuse and cost spikes
- Timeouts so users aren’t stuck waiting
- Fallback behaviour (templates, search, manual workflow, “contact support”)
Acceptance criteria: when the AI provider is unavailable, users can still complete the core journey.
7) Output guardrails: brand, safety, and compliance
For user‑facing outputs (support chat, generated content, recommendations), request guardrails such as:
- Content filtering (hate/violence/sexual content)
- Domain‑specific restrictions (e.g., no medical/legal/financial advice)
- Brand voice constraints and approved wording for critical areas
Acceptance criteria: a small “tricky prompt” test suite is blocked or safely handled with predictable outcomes.
8) Don’t forget the basics: security still matters
AI features often introduce new tokens, keys, and data flows. Require standard controls:
- TLS everywhere
- Secrets stored server‑side (not in the client app)
- Least‑privilege access for internal tools and logs
Acceptance criteria: API keys are not exposed in browser/mobile code and access is role‑based.
A simple acceptance test you can request before launch
Ask your development team to demo these five things before final sign‑off: (1) the consent flow, (2) retention settings, (3) redaction using sample PII, (4) AI downtime fallback, and (5) a short “bad prompt” suite with expected outcomes.
If you’re considering AI-support chat, content generation, recommendations, internal automation-we can help you design and implement it so it’s useful, safe, and shippable. Get in touch with Jensen Technologies if you’d like to discuss your use case or want help putting these safeguards into your next build.